import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)




def 							UpperCamelCase		(			__lowercase		:					nn.ModuleList		,__lowercase		:					nn.ModuleList		,__lowercase		:					List[int]      ):
		'''simple docstring'''




		A_						:							Dict      = nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
		assert len(__lowercase      ) == len(__lowercase      ), f'''{len(__lowercase      )} != {len(__lowercase      )}'''
		dest_layers.load_state_dict(layers_to_copy.state_dict()      )


_UpperCAmelCase          =						{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_UpperCAmelCase          =						{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}




def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Union[str, Any]      ):
		'''simple docstring'''




		try:
				A_						:							Optional[int]      = LAYERS_TO_COPY[n_teacher][n_student]
				return val
		except KeyError:
				if n_student != n_teacher:
						warnings.warn(
						    f'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
						    f''' {n_student}'''      )
				return list(range(__lowercase      )      )




def 							UpperCamelCase		(			__lowercase		:					Optional[int]		,__lowercase		:					Tuple      ):
		'''simple docstring'''




		if n_student > n_teacher:
				raise ValueError(f'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''      )
		elif n_teacher == n_student:
				return list(range(__lowercase      )      )
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]




def 							UpperCamelCase		(			__lowercase		:					Union[str, PreTrainedModel]		,__lowercase		:					Union[str, Path] = "student"		,__lowercase		:					Union[int, None] = None		,__lowercase		:					Union[int, None] = None		,__lowercase		:					Tuple=False		,__lowercase		:					int=None		,__lowercase		:					Optional[Any]=None		,**__lowercase		:					str		,):
		'''simple docstring'''




		A_						:							Union[str, Any]      = 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
		assert (e is not None) or (d is not None), _msg
		if isinstance(__lowercase		,__lowercase      ):
				AutoTokenizer.from_pretrained(__lowercase      ).save_pretrained(__lowercase      )  # purely for convenience
				A_						:							Tuple      = AutoModelForSeqaSeqLM.from_pretrained(__lowercase      ).eval()
		else:
				assert isinstance(__lowercase		,__lowercase      ), f'''teacher must be a model or string got type {type(__lowercase      )}'''
		A_						:							str      = teacher.config.to_diff_dict()

		try:
				A_  ,     A_						:							Any      = teacher.config.encoder_layers, teacher.config.decoder_layers
				if e is None:
						A_						:							Any      = teacher_e
				if d is None:
						A_						:							Union[str, Any]      = teacher_d
				init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}      )
		except AttributeError:  # T5
				if hasattr(teacher.config		,'num_encoder_layers'      ):
						A_  ,     A_						:							List[str]      = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
				else:
						A_  ,     A_						:							str      = teacher.config.num_layers, teacher.config.num_decoder_layers
				if e is None:
						A_						:							Optional[Any]      = teacher_e
				if d is None:
						A_						:							Tuple      = teacher_d
				if hasattr(teacher.config		,'num_encoder_layers'      ):
						init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}      )
				else:
						init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}      )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
		init_kwargs.update(__lowercase      )

		# Copy weights
		A_						:							Tuple      = teacher.config_class(**__lowercase      )
		A_						:							List[str]      = AutoModelForSeqaSeqLM.from_config(__lowercase      )
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		A_						:							Dict      = student.load_state_dict(teacher.state_dict()		,strict=__lowercase      )
		assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

		if copy_first_teacher_layers:  # Our copying is done. We just log and save
				A_  ,     A_						:							Optional[Any]      = list(range(__lowercase      )      ), list(range(__lowercase      )      )
				logger.info(
				    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
				    f''' {save_path}'''      )
				student.save_pretrained(__lowercase      )
				return student, e_layers_to_copy, d_layers_to_copy

		# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
		if e_layers_to_copy is None:
				A_						:							List[int]      = pick_layers_to_copy(__lowercase		,__lowercase      )
		if d_layers_to_copy is None:
				A_						:							List[int]      = pick_layers_to_copy(__lowercase		,__lowercase      )

		try:
				if hasattr(
				    __lowercase		,'prophetnet'      ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers		,student.prophetnet.encoder.layers		,__lowercase      )
						copy_layers(teacher.prophetnet.decoder.layers		,student.prophetnet.decoder.layers		,__lowercase      )
				else:
						copy_layers(teacher.model.encoder.layers		,student.model.encoder.layers		,__lowercase      )
						copy_layers(teacher.model.decoder.layers		,student.model.decoder.layers		,__lowercase      )
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block		,student.encoder.block		,__lowercase      )
				copy_layers(teacher.decoder.block		,student.decoder.block		,__lowercase      )
		logger.info(
		    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''      )
		A_						:							Tuple      = {
		    'teacher_type': teacher.config.model_type,
		    'copied_encoder_layers': e_layers_to_copy,
		    'copied_decoder_layers': d_layers_to_copy,
		}
		student.save_pretrained(__lowercase      )
		# Save information about copying for easier reproducibility

		return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
	fire.Fire(create_student_by_copying_alternating_layers)







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_UpperCAmelCase          =						collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase          =						"""https://storage.googleapis.com/cvdf-datasets/mnist/"""




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Tuple      = numpy.dtype(numpy.uintaa      ).newbyteorder('>'      )
		return numpy.frombuffer(bytestream.read(4      )		,dtype=__lowercase      )[0]




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							str      = _readaa(__lowercase      )
				if magic != 20_51:
						raise ValueError(
						    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)      )
				A_						:							Optional[int]      = _readaa(__lowercase      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Any      = _readaa(__lowercase      )
				A_						:							List[str]      = bytestream.read(rows * cols * num_images      )
				A_						:							Optional[int]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				A_						:							Union[str, Any]      = data.reshape(__lowercase		,__lowercase		,__lowercase		,1      )
				return data




@deprecated(__lowercase		,'Please use tf.one_hot on tensors.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[int]		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Dict      = labels_dense.shape[0]
		A_						:							List[str]      = numpy.arange(__lowercase      ) * num_classes
		A_						:							Any      = numpy.zeros((num_labels, num_classes)      )
		A_						:							List[Any]      = 1
		return labels_one_hot




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Tuple=False		,__lowercase		:					List[str]=10      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							int      = _readaa(__lowercase      )
				if magic != 20_49:
						raise ValueError(
						    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Dict      = bytestream.read(__lowercase      )
				A_						:							List[str]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				if one_hot:
						return _dense_to_one_hot(__lowercase		,__lowercase      )
				return labels





class UpperCAmelCase		:


				'''simple docstring'''






				@deprecated(
				    lowercase						,      'Please use alternatives such as official/mnist/_DataSet.py'
				    ' from tensorflow/models.'						,      )
				def __init__(		self						,      lowercase						,      lowercase						,      lowercase=False						,      lowercase=False						,      lowercase=dtypes.floataa						,      lowercase=True						,      lowercase=None						,      ):



						"""simple docstring"""





						A_  ,     A_						:							Dict      = random_seed.get_seed(lowercase   )
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda   )
						A_						:							Optional[int]      = dtypes.as_dtype(lowercase   ).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype   )
						if fake_data:
								A_						:							Optional[int]      = 1_0_0_0_0
								A_						:							int      = one_hot
						else:
								assert (
								    images.shape[0] == labels.shape[0]
								), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
								A_						:							Dict      = images.shape[0]

								# Convert shape from [num examples, rows, columns, depth]
								# to [num examples, rows*columns] (assuming depth == 1)
								if reshape:
										assert images.shape[3] == 1
										A_						:							int      = images.reshape(
										    images.shape[0]						,      images.shape[1] * images.shape[2]   )
								if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										A_						:							Dict      = images.astype(numpy.floataa   )
										A_						:							Optional[int]      = numpy.multiply(lowercase						,      1.0 / 255.0   )
						A_						:							Optional[int]      = images
						A_						:							List[Any]      = labels
						A_						:							str      = 0
						A_						:							Union[str, Any]      = 0
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._images
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._labels
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._num_examples
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._epochs_completed







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=False						,      lowercase=True   ):



						"""simple docstring"""





						if fake_data:
								A_						:							List[Any]      = [1] * 7_8_4
								A_						:							Dict      = [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(lowercase   )],
								    [fake_label for _ in range(lowercase   )],
								)
						A_						:							int      = self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
								A_						:							Tuple      = numpy.arange(self._num_examples   )
								numpy.random.shuffle(lowercase   )
								A_						:							Dict      = self.images[perma]
								A_						:							Optional[Any]      = self.labels[perma]
						# Go to the next epoch
						if start + batch_size > self._num_examples:
								# Finished epoch
								self._epochs_completed += 1
								# Get the rest examples in this epoch
								A_						:							List[Any]      = self._num_examples - start
								A_						:							Optional[int]      = self._images[start : self._num_examples]
								A_						:							Optional[int]      = self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
										A_						:							Tuple      = numpy.arange(self._num_examples   )
										numpy.random.shuffle(lowercase   )
										A_						:							Dict      = self.images[perm]
										A_						:							Any      = self.labels[perm]
								# Start next epoch
								A_						:							Any      = 0
								A_						:							Optional[Any]      = batch_size - rest_num_examples
								A_						:							Optional[int]      = self._index_in_epoch
								A_						:							Dict      = self._images[start:end]
								A_						:							Any      = self._labels[start:end]
								return (
								    numpy.concatenate((images_rest_part, images_new_part)						,      axis=0   ),
								    numpy.concatenate((labels_rest_part, labels_new_part)						,      axis=0   ),
								)
						else:
								self._index_in_epoch += batch_size
								A_						:							Tuple      = self._index_in_epoch
								return self._images[start:end], self._labels[start:end]




@deprecated(__lowercase		,'Please write your own downloading logic.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					Optional[int]		,__lowercase		:					Any      ):
		'''simple docstring'''




		if not gfile.Exists(__lowercase      ):
				gfile.MakeDirs(__lowercase      )
		A_						:							Optional[Any]      = os.path.join(__lowercase		,__lowercase      )
		if not gfile.Exists(__lowercase      ):
				urllib.request.urlretrieve(__lowercase		,__lowercase      )  # noqa: S310
				with gfile.GFile(__lowercase      ) as f:
						A_						:							Optional[int]      = f.size()
				print('Successfully downloaded'		,__lowercase		,__lowercase		,'bytes.'      )
		return filepath




@deprecated(
    __lowercase		,'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'      )
def 							UpperCamelCase		(			__lowercase		:					Dict		,__lowercase		:					str=False		,__lowercase		:					int=False		,__lowercase		:					str=dtypes.floataa		,__lowercase		:					int=True		,__lowercase		:					List[Any]=50_00		,__lowercase		:					Union[str, Any]=None		,__lowercase		:					Union[str, Any]=DEFAULT_SOURCE_URL		,):
		'''simple docstring'''




		if fake_data:

				def fake():
						return _DataSet(
						    []		,[]		,fake_data=__lowercase		,one_hot=__lowercase		,dtype=__lowercase		,seed=__lowercase      )

				A_						:							Union[str, Any]      = fake()
				A_						:							Any      = fake()
				A_						:							str      = fake()
				return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )

		if not source_url:  # empty string check
				A_						:							Tuple      = DEFAULT_SOURCE_URL

		A_						:							List[Any]      = 'train-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 'train-labels-idx1-ubyte.gz'
		A_						:							Optional[Any]      = 't10k-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 't10k-labels-idx1-ubyte.gz'

		A_						:							str      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Optional[int]      = _extract_images(__lowercase      )

		A_						:							Optional[Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Union[str, Any]      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		A_						:							Any      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							str      = _extract_images(__lowercase      )

		A_						:							Union[str, Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							int      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		if not 0 <= validation_size <= len(__lowercase      ):
				A_						:							Dict      = (
				    'Validation size should be between 0 and '
				    f'''{len(__lowercase      )}. Received: {validation_size}.'''
				)
				raise ValueError(__lowercase      )

		A_						:							int      = train_images[:validation_size]
		A_						:							List[str]      = train_labels[:validation_size]
		A_						:							Tuple      = train_images[validation_size:]
		A_						:							Dict      = train_labels[validation_size:]

		A_						:							str      = {'dtype': dtype, 'reshape': reshape, 'seed': seed}

		A_						:							Optional[int]      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )

		return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )






